Regarding ensemble implementations, in [1]}, their RF performed more accurately than the MLP-ANN, \(k\) -NN and AdaBoost implementations. Similar results were observed in [2]}, where the author's RF implementation outperformed the \(k\) -NN, SVR, Adaboost and the gradient tree boosting (GTB) methods. However, it should be noted that all 5 ML models were close, accuracy-wise. Finally, a voting regressor (VR) ensemble model constructed from these 5 learners was slightly more accurate. One more paper that found RF to be the most accurate model is [3]}, where the authors compared it against an empirical model (log-distance) and 3 other ML models; a single hidden-layer ANN, SVR and AdaBoost. RF was the most accurate model across all three frequencies the authors used (2.4, 3.52 and 5.8 GHz). In [4]}, the RF implementation was again found more accurate than the MLP-ANN and the SVR. Finally, in [5]}, the authors compared AdaBoost, RF, \(k\) -NN, SVR and ANN regression algorithms against Lasso regression [6]} (a type of linear restricted regression) and the kriging algorithm
(often used in geostatistics). The goal was to predict the RSS of a digital terrestrial television (DTT) system at any point inside a coverage area and project it on Google maps. They found that all ML models were noticeably more accurate than kriging and Lasso. Among the ML models, the most accurate were AdaBoost and the RF regression algorithm, exhibiting similar performance.
